Journal article

Prediction of Sewer Condition Grade Using Support Vector Machines

John Mashford, David Marlow, Dung Tran, Robert May

JOURNAL OF COMPUTING IN CIVIL ENGINEERING | ASCE-AMER SOC CIVIL ENGINEERS | Published : 2011

Abstract

Assessing the condition of sewer networks is an important asset management approach. However, because of high inspection costs and limited budget, only a small proportion of sewer systems may be inspected. Tools are therefore required to help target inspection efforts and to extract maximum value from the condition data collected. Owing to the difficulty in modeling the complexities of sewer condition deterioration, there has been interest in the application of artificial intelligence-based techniques such as artificial neural networks to develop models that can infer an unknown structural condition based on data from sewers that have been inspected. To this end, this study investigates the ..

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University of Melbourne Researchers

Grants

Funding Acknowledgements

The authors gratefully acknowledge the funding provided by UWI and the CSIRO Water for a Healthy Country Flagship for this research. The anonymous internal and external reviewers are also gratefully acknowledged. Finally, thank go to staff members at UWI and CSIRO for their kind support, especially project team members Fanny Boulaire, David Beale, and Mike Rahilly.